Cost of the process of app from acquiring and converting users has become very expensive. And new user operations with ASO has been recognized as a major trend in UK.
Nowadays, the cost of the process of acquiring target users and then converting them into users of their own products has become very expensive. And in such a brutal competitive environment, it is a recognized trend in the industry to do well in new user operation with the help of data intelligence.
However, how to use data intelligence technology to maximize the growth effect is a discipline. Today, we will talk about how to solve the increase and growth of new app users through user portraits?
As the traffic dividend is fading away, where can app go to ask for user growth? Some app choose to convert from super users; some app choose to sink the market to get more new traffic; some app increase the incentive chip of user sharing in exchange for user fission ...... but the anxiety of app operation never stops, how long can these methods maintain the growth? Where is the next growth point of user operation?
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Let's dive into the definition of 'active' for your site or app. Is signing-in sufficient? Does reading one piece of content qualify a user as being active? Or does a user have to engage by liking, commenting, sharing, or downloading to be considered active?
The definition is unique to each business. It depends on the purpose of your app and the user actions that are most valuable to your business. There are two main things to keep in mind.
The action needs to relate to what you are trying to accomplish with your app or site. Let's say your app offers vacation home rentals around the world. Someone who frequently reads reviews of popular locations has little value to your business.
An active user may not always be an engaged user. If engagement isn't contributing to retention, it may not be relevant.
This one can cause serious confusion and mess up your analysis. You need to keep the definition consistent across the board. Make sure everyone on the team and every stakeholder knows what an active user means and what it doesn't.
From a business perspective, it is important to know that your app is actually being used and is useful to your customers. Active users indicate that people are interacting with your service or product. A healthy number of active users is therefore a sign you are doing things right for your users.
Determining the number of active users over time helps with assessing the effectiveness of your marketing campaigns and the customer experience. It is also important in calculating other key metrics. For example, the lifetime value of customers cannot be calculated without knowing your retention rates, and customer retention rates rely on data on whether users are active over time.
So, to put it simply, the number of active users provides a measure of the general health of the business and serves as a basis to calculate other, more informative metrics.
The growth of new users contains three stages, the stage of new user acquisition, the stage of new user conversion, and the stage of continuous keeping them active, and the role played by user portraits in different stages varies.
App in the customer acquisition stage mainly relies on precise placement, where precision has two layers of meaning, one is the accurate reaching of users, and the other is how to get the most high-value users with the lowest cost. In specific implementation, target users, channels and content are indispensable, and user portraits can focus the energy of the three and maximize the effect.
Before precise placement, app needs to clarify its target users and understand the characteristics of the target user group, app can use the tool of user portrait to conduct data analysis and big data modeling on the most active, interactive and consumption habit user groups on its app, so as to transform the abstract user definition into data tags and characteristics and set clear targets for customer acquisition strategy.
In terms of channel screening, app can not only use the target user portrait for channel matching, but also retrospectively verify the previous placement data, and finally select media preferably from various aspects such as quantity, TA concentration, price and integrity.
In terms of content creativity, app can group the target group creatively through portrait, and get the favor of high-value users through customized content, instead of blindly pursuing low-cost users. For example, the information app likes to use the hunting class material as the content, because this kind of material is easy to produce explosive, and it is easy to get users at a very low cost. But for some news app with attitude, depth and insight will not use curiosity material, because these contents can't bring high value users who have requirements for content depth and are willing to pay for good content.
At present, the big manufacturers have begun to pay attention to their own portrait system and use it to derive more complex growth models, and its effect is also remarkable. A well-known news app has chosen channels with high concentration of high-value users through model algorithms, and selected in-depth report content or exclusive news to acquire target users. According to statistics, the contribution of this growth strategy to the overall DAU of the app has increased by more than 10%, achieving the customer acquisition goal of getting more effective users with the same amount of money.
Users' preference of using app has "first cause effect", just like people order take-out, which restaurant has good taste, good packaging and good service when they order it for the first time, then the chance of ordering take-out in this restaurant will be higher. Therefore, how to make a good impression on new users when they come into the app for the first time and make them like your product is the main battlefield of PK between app operations.
First of all, to understand every new user who enters app, which requires app to know the new user portrait as accurately as possible.
In the first step, app guides through the opening screen and lets new users fill in or check off basic information, interests and other characteristics. But this information does not represent the user, one is not objective and accurate enough, and the other is far from the amount of information to generate the user portrait.
In the second step, app can make up a new user portrait quickly with the ability of third-party data companies. Third-party data companies have more comprehensive data dimensions and deeper deposits of users' online behavioral preferences, which are new users to app, but may be familiar users in the third-party database.
In the third step, app can go back to the characteristics of the customer acquisition channel and the material placed, and synthesize these characteristics to make the new user portrait as complete as possible.
In this way, app can fully understand the interests and preferences of new users and recommend the content, services or goods they need and like to new users.
Secondly, after understanding the user profile, app can also use the algorithm to label the characteristics of new users and associate them with people who have similar characteristics in the existing user base, so as to recommend content to new users through synergy.
During the cold start, the app will also make additions with the help of third-party user tags, but how to integrate the two tagging systems? After practice, we proposed a better solution, which is to gather the third-party tags into a group of new users through the theme model algorithm, and then carry out subgroup operation.
An information app had cooperated with personal push to divide new users into groups and make personalized recommendations according to the calculation results. For example, if a new user group has 60% chance of being fashion and entertainment people, 25% chance of being mother and baby people, and 15% chance of being online shopping people, then it can recommend fashion and entertainment content to this group, followed by mother and baby content and consumer content, and pay attention to their reading situation and make dynamic adjustments. At the same time, the clustering characteristics of this group can also be made a synergy with the existing user data, and the content with the best clicks among similar people among existing users can be recommended. Eventually, the next-day retention rate of new users of the app increased by 18%.
Finally, in the part of content recommendation, app can not simply rely on data, manual operation is also very important. Manual operation can provide users with emotional and warm operation measures.
The cold start of new users is a continuous process, and it needs to change according to the situation and time to achieve the real sense of titillating users and retaining them.
In the cold start process, app also needs to make users return to the product often through various operation means and product design. For example, in operation, set the corner mark unread to stimulate the user's compulsion to click to activate. In terms of product functions, app needs to do a good job of designing user incentives such as newbie guidance, point mall and incentive activities to give users enough sense of achievement.
In the process of user cold start, the iteration of user portrait needs to continue to advance. app can continue to carry out portrait insight to the active users, and make good synergy between new user data and existing user data, so as to better understand the users, and constantly make growth assumptions, growth strategies, and continue to detect the interests of users through the operation of content, goods, activities, and finally find the optimal solution to meet the needs of users. We will continue to make growth assumptions and growth strategies, and continue to detect users' interests through operational content, products and activities to find the optimal solution to meet users' needs.
User Acquisition,User Retention,
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